Learning from data: A post classification method for annual land cover analysis in urban areas

Shishi Liu, Hang Su, Guofeng Cao, Shanqin Wang, Qingfeng Guan

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Annual analyses of land cover dynamics in urban areas provide a thorough understanding of the urbanization effects on environment and valuable information for the improvement of urban growth modeling. However, most current studies focus on major land cover changes, such as urbanization and vegetation loss. The most feasible way to evaluate the complex interactions among different land cover types is the post-classification change detection, but the temporal inconsistency in the time series of land cover maps impedes the high-frequency and long-term analyses. This study proposed a spatio-temporal land cover filter (STLCF) to remove the illogical land cover change events in the time series of land cover maps, and analyzed the annual land cover dynamics in urban areas. The knowledge of illogical land cover change events was ‘learned’ from the land cover maps through the spatio-temporal transition probability matrix, instead of experts’ knowledge. The illogical change was modified with the land cover of the maximum probability calculated from the naïve Bayesian equation. The STLCF was tested in Wuhan, a typical densely urbanized Chinese city. The annual land cover maps from 2000 to 2013 were derived from multi-date Landsat images using the Decision Tree (DT) classifier. Results showed that the STLCF improved the mean overall accuracy of annual change detection by about 6%. Additionally, the amount of land cover trajectories with unrealistically frequent changes was significantly decreased. During the study period, 7.86% of the pixels experienced one land cover change, and about 0.57% of the pixels experienced land cover changes more than once. The annual analyses demonstrated the non-linear increasing trend in urbanization as well as the corresponding trend in vegetation loss in the study area. We also found the conversion from built-up areas to vegetation near rivers and lakes and in the reserves and rural areas, mainly caused by the restoration of built-up areas to the park or green belt/wedges along rivers and new roads in the metropolitan areas, and to the cropland and woods in the rural areas. Results of this study showed the importance of the spatio-temporal consistency check with knowledge derived from land cover maps of the study area, which facilitates the annual analyses of major and subtle land cover dynamics in urban areas.

Original languageEnglish
Pages (from-to)202-215
Number of pages14
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Aug 2019


  • Annual land cover change detection
  • Spatio-temporal land cover filter
  • Urban area


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